SDM with R

1st QCBS R Symposium

Outline

This tutorial illustrates how to build, evaluate and project species distribution models within R. The main steps we will go through, described bellow, are the following:

  1. R environment preparation
  2. Data preparation
    • Study area and manipulating spatial information
    • Environmental data
    • Species occurrence data
      • Presence-and-absence data
      • Presence-only and pseudo-absences data
  3. Model fitting, prediction, and evaluation
  4. Projecting models
  5. Multi-species modelling
  6. DIY: Create a SDM yourself!
  7. Bonus

R environment preparation

Download the .zip file from here and extract files to your favorite QCBS R Symposium workshop folder.

To run this workshop, please use the latest version of R. A certain number of libraries are also required and will be downloaded. Install and load all required R libraries:

install.packages("rgdal")
install.packages("raster")
install.packages("rgeos")
install.packages("dismo")
install.packages("letsR")
install.packages("biomod2")
install.packages("biogeo")
install.packages("maptools")
install.packages("stringr")
install.packages("ggplot2")
install.packages("plotly")
install.packages("xtable")
library(rgdal)
library(raster)
library(rgeos)
library(dismo)
library(letsR)
library(biomod2)
library(biogeo)
library(maptools)
library(stringr)
library(ggplot2)
library(plotly)
library(xtable)

Set your working directory to the same directory of the folder workshopSDM using the function setwd() or your prefered method.

Finally, we recommend to save and create a new working environment for this tutorial.

# save all the working space
save.image("QCBS_SDMTutorial.RData")
# free the working space
rm(list = ls())
# and get it back
load("QCBS_SDMTutorial.RData")

Data preparation

Selecting your study area and manipulating spatial files

Most species distribution models are done using spatial grid information. In the next step, we will learn how to create a polygon grid of a given region, which will be used within our species distribution model framework.

For this tutorial, we have chosen the Neotropical zoogeographical region as our study area. Load the polygon shapefile of it using the readOGR function:

neotropical_shape <- rgdal::readOGR("data/shapefiles/neotropical.shp")
OGR data source with driver: ESRI Shapefile 
Source: "data/shapefiles/neotropical.shp", layer: "neotropical"
with 1 features
It has 3 fields
plot(neotropical_shape)

Now that we have our study region, we need to create a grid, intersect our shapefile and create a new grid of that region. For this, we will use the modified GridFilter function (originally from Hidasi-Neto, J.). It allows us to input a polygon shapefile, specify a resolution and the proportion of overlap for each cell to be considered.

We will apply the function to the polygon neotropical_shape you have loaded.

GridFilter <- function(shape, resol = 1, prop = 0) {
    grid <- raster(extent(shape))
    res(grid) <- resol
    proj4string(grid) <- proj4string(shape)
    gridpolygon <- rasterToPolygons(grid)
    drylandproj <- spTransform(shape, CRS("+proj=laea"))
    gridpolproj <- spTransform(gridpolygon, CRS("+proj=laea"))
    gridpolproj$layer <- c(1:length(gridpolproj$layer))
    areagrid <- gArea(gridpolproj, byid = T)
    gridpolproj <- gBuffer(gridpolproj, byid = TRUE, width = 0)
    dry.grid <- intersect(drylandproj, gridpolproj)
    areadrygrid <- gArea(dry.grid, byid = T)
    info <- cbind(dry.grid$layer, areagrid[dry.grid$layer], areadrygrid)
    dry.grid$layer <- info[, 3]/info[, 2]
    dry.grid <- spTransform(dry.grid, CRS(proj4string(shape)))
    dry.grid.filtered <- dry.grid[dry.grid$layer >= prop, ]
}

# Create a spatial polygon grid for the Neotropical region, with 5 degrees x
# 5 degrees

neotropical_grid <- GridFilter(neotropical_shape, resol = 5, prop = 0.5)

## Create an ID field for it, which will be useful in the future
neotropical_grid$ID <- 1:(length(neotropical_grid))
# Export your resulting polygon grid
writeOGR(neotropical_grid, dsn = paste(getwd(), "/data/shapefiles", sep = ""), 
    layer = "neotropical_grid_5", driver = "ESRI Shapefile", overwrite_layer = T)

This is our resulting polygon grid, where each cell refers to a site (and a row) in our data.

neotropical_grid <- readOGR("data/shapefiles/neotropical_grid_5.shp")
OGR data source with driver: ESRI Shapefile 
Source: "data/shapefiles/neotropical_grid_5.shp", layer: "neotropical_grid_5"
with 63 features
It has 5 fields
plot(neotropical_grid)

Importing environmental variables

In species distribution modeling, predictor variables are typically extracted from raster (grid) type files. Each ‘raster’ should represent a given variable of interest, which can be climatic, soil, terrain, vegetation, land use, and other types variables.

The getData function from the dismo package is able to retrieve useful climate, elevation and administrative data.

bioClimVars <- getData("worldclim", var = "bio", res = 10)
bioClimVars

You can also load your predictor variables from any directory within your computer using the raster (single file) and stack (loads multiple files) functions:

rasterFiles <- list.files("data/rasters/bio_10m_bil", pattern = "bil$", full.names = TRUE)
rasterFiles
 [1] "data/rasters/bio_10m_bil/bio1.bil" 
 [2] "data/rasters/bio_10m_bil/bio10.bil"
 [3] "data/rasters/bio_10m_bil/bio11.bil"
 [4] "data/rasters/bio_10m_bil/bio12.bil"
 [5] "data/rasters/bio_10m_bil/bio13.bil"
 [6] "data/rasters/bio_10m_bil/bio14.bil"
 [7] "data/rasters/bio_10m_bil/bio15.bil"
 [8] "data/rasters/bio_10m_bil/bio16.bil"
 [9] "data/rasters/bio_10m_bil/bio17.bil"
[10] "data/rasters/bio_10m_bil/bio18.bil"
[11] "data/rasters/bio_10m_bil/bio19.bil"
[12] "data/rasters/bio_10m_bil/bio2.bil" 
[13] "data/rasters/bio_10m_bil/bio3.bil" 
[14] "data/rasters/bio_10m_bil/bio4.bil" 
[15] "data/rasters/bio_10m_bil/bio5.bil" 
[16] "data/rasters/bio_10m_bil/bio6.bil" 
[17] "data/rasters/bio_10m_bil/bio7.bil" 
[18] "data/rasters/bio_10m_bil/bio8.bil" 
[19] "data/rasters/bio_10m_bil/bio9.bil" 
# Stack the bioclimatic variables
bioClimVars <- stack(rasterFiles)
bioClimVars
class       : RasterStack 
dimensions  : 900, 2160, 1944000, 19  (nrow, ncol, ncell, nlayers)
resolution  : 0.1666667, 0.1666667  (x, y)
extent      : -180, 180, -60, 90  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs 
names       :  bio1, bio10, bio11, bio12, bio13, bio14, bio15, bio16, bio17, bio18, bio19,  bio2,  bio3,  bio4,  bio5, ... 
min values  :  -269,   -97,  -488,     0,     0,     0,     0,     0,     0,     0,     0,     9,     8,    72,   -59, ... 
max values  :   314,   380,   289,  9916,  2088,   652,   261,  5043,  2159,  4001,  3985,   211,    95, 22673,   489, ...

You can also plot your variables to take a look at them:

plot(bioClimVars/10)  # WorldClim data is multiplied by 10. Let's take a look at the real information.

Variable extraction

Before extracting variables, it is really important that your rasters have the same projection (i.e. type of coordinate reference system) as your other spatial files. See ?CRS for help in finding CRS definitions.

crs.geo <- CRS("+proj=longlat +ellps=WGS84 + datum=WGS84")  # geographical, datum WGS84

neotropical_grid <- spTransform(neotropical_grid, crs.geo)  # define projection system of grid data
proj4string(bioClimVars) <- crs.geo  # and of our bioclimatic rasters

Now, we can extract the predictor values for each cell of our grid!

myExpl <- data.frame(bio_1 = numeric(length(neotropical_grid)), bio_2 = numeric(length(neotropical_grid)), 
    bio_3 = numeric(length(neotropical_grid)), bio_4 = numeric(length(neotropical_grid)))

bio_1_ext <- extract(bioClimVars$bio1, neotropical_grid)  # bio_1_ext is a list of cells that contains all values for each cell, for each predictor
bio_2_ext <- extract(bioClimVars$bio2, neotropical_grid)
bio_3_ext <- extract(bioClimVars$bio3, neotropical_grid)
bio_4_ext <- extract(bioClimVars$bio4, neotropical_grid)

myExpl$bio_1 <- unlist(lapply(bio_1_ext, function(x) if (!is.null(x)) mean(x, 
    na.rm = TRUE) else NA))

myExpl$bio_2 <- unlist(lapply(bio_2_ext, function(x) if (!is.null(x)) mean(x, 
    na.rm = TRUE) else NA))

myExpl$bio_3 <- unlist(lapply(bio_3_ext, function(x) if (!is.null(x)) mean(x, 
    na.rm = TRUE) else NA))

myExpl$bio_4 <- unlist(lapply(bio_4_ext, function(x) if (!is.null(x)) mean(x, 
    na.rm = TRUE) else NA))

write.table(myExpl, "data/matrices/NT_EnvVar_5.txt")

# Let's take a look at our table:
head(myExpl)
     bio_1    bio_2    bio_3    bio_4
1 236.3584 112.6243 53.09249 3409.509
2 215.4862 146.0966 67.23103 1670.731
3 185.2692 151.6923 68.04196 1806.381
4 256.4933 113.5570 64.12752 1954.671
5 235.1353 114.5294 74.07059 1250.724
6 217.4312 125.9777 71.84015 1245.725

Importing species data

Using expert drawn maps

One way of acquiring species distribution information is from expert-drawn maps, such as the ones from the IUCN Red List for Threatened Species database. These maps Tare usually done used multiple methods: only on occurrence data (e.g. a polygon around known occurrences), whereas others incorporate multiple degrees of knowledge, such as habitat requirements or elevational limits of the species, in essence using an informal distribution modelling approach.

Richness maps derived from expert-drawn range maps may overestimate local richness (‘error of commission’), in relation to point-to-grid richness maps. This because they are generally drawn to include all areas where a species is known to occur without excluding areas in between where the species may not exist. They tend to map the ‘extent of occurrence’ of species that includes the, perhaps much smaller, ‘area of occupancy’ (Loiselle et al., 2003; Habib et al., 2004; Hurlbert & White, 2005; Graham & Hijmans, 2006).

We advise caution when using them.

# Load your species map file
speciesGeoDist <- readShapePoly("data/shapefiles/NT_TERRESTRIAL_MAMMALS_subset.shp", 
    proj4string = crs.geo)

plot(speciesGeoDist)

Create a presence-absence matrix of species’ geographic ranges within your polygon grid shapefile. For this, we will use the function lets.presab.grid from the letsR package.

crs.geo <- CRS("+proj=longlat +ellps=WGS84 + datum=WGS84")  # geographical, datum WGS84

proj4string(neotropical_grid) <- crs.geo  # define projection system of grid data
proj4string(speciesGeoDist) <- crs.geo  # and of our bioclimatic rasters

abspres.matrix <- lets.presab.grid(shapes = speciesGeoDist, grid = neotropical_grid, 
    sample.unit = "ID", remove.sp = TRUE, presence = NULL, origin = NULL, seasonal = NULL)

# Export your presence-absence matrix
write.table(abspres.matrix$PAM, "data/matrices/NT_PAM_5.txt")

You can now visualize a map of your species richness within your polygon grid:

richnessCalc <- rowSums(abspres.matrix$PAM) + 1
colPalette <- colorRampPalette(c("#fff5f0", "#fb6a4a", "#67000d"))
colors <- c("white", colPalette(max(richnessCalc)))
plot(abspres.matrix$grid, border = "gray40", col = colors[richnessCalc])
map(add = TRUE)

We also need the coordinates of our centroids to proceed with the data analysis:

# Extract coordinates from the cell centroids
myRespCoord <- as.data.frame(coordinates(abspres.matrix$grid))
colnames(myRespCoord) <- c("Longitude_X", "Latitude_Y")

# Export it for the future
write.table(myRespCoord, "data/matrices/NT_coords_5.txt")

Using occurrence records

We can also use occurrence records available online, or collected in the field. Here, we will use information from the Global Biodiversity Information Facility and import it using the function gbif:

D_youngi.GBIF <- gbif("Diaemus", "youngi")
# get data frame with spatial coordinates (points)
Dyoungi.Occ <- subset(D_youngi.GBIF, select = c("country", "lat", "lon"))
head(Dyoungi.Occ)  # a simple data frame with coordinates
              country      lat       lon
1           Nicaragua 11.11199 -85.76016
2 Trinidad and Tobago 10.54603 -61.48618
3            Suriname  1.99449 -56.09211
4            Suriname  1.99000 -56.09000
5            Suriname  1.99400 -56.09200
6            Suriname       NA        NA

Issues with occurrence points

Occurrence data from museum and herbarium collections are very valuable for mapping biodiversity patterns, and for species distribution modelling. But these datasets contain a lot of errors and are susceptible to many issues relating data quality. Many of these errors are extensively described in “Biogeo: an R package for assessing and improving data quality of occurrence record datasets”, from Robertson et al., 2016

Let’s take a look at a few of them:

Description of errors detected in point data with explanations of the likely cause of the error. Yesson et al. (2007) described several errors, which we give in brackets in the Error column; copied from Robertson et al. 2016
Error Probable.Reason
Points plotted at zero degrees latitude and longitude. No coordinates were available in the original dataset but values of zero assigned to the coordinates
Points in sea for terrestrial species or on land for aquatic species, obvious geographical outliers. Transposed latitude and longitude coordinates; incorrect sign on the decimal degrees of the latitude or longitude coordinate; degrees and minutes were transposed before the coordinate was converted to decimal degrees; imprecise locality description used to assign coordinates; the specimen was incorrectly identified by the collector or the incorrect name was applied to the species when the data were digitized.
Point in sea but close to coast for terrestrial species, or on land but close to coast for marine species. Low precision coordinates e.g. only degrees were available or the data were originally collected on a coarse-scale grid. Imprecise locality description used to assign coordinates.
Point plotted along the prime meridian or equator. Missing coordinate for latitude or for longitude that was incorrectly assigned a value of zero.
Country name given in the record does not correspond with country where point is plotted. Likely to be the same errors as for b) above.
Elevation given in the record does not correspond with elevation obtained from a digital elevation model where point is plotted. Likely to be the same errors as for b) above, or the spatial resolution of the digital elevation model is too coarse.

One way of working around these issues is by using the packages biogeo, which has many functions for quality assessment and coordinate conversion.

Data cleaning

Let’s take a look at our species matrix. What is going when we look at the sum of the columns (i.e. number of presences)?

# Load our species data
DataSpecies <- read.table("data/matrices/NT_PAM_5.txt", header = TRUE)

colSums(DataSpecies)
            Panthera.onca             Pecari.tajacu 
                      131                       150 
      Peromyscus.leucopus   Phyllostomus.latifolius 
                        3                        25 
        Philander.opossum     Phyllomys.nigrispinus 
                       92                         5 
     Peromyscus.mexicanus       Phyllomys.dasythrix 
                        6                         7 
   Oxymycterus.amazonicus      Oxymycterus.hispidus 
                       16                        10 
    Peromyscus.pectoralis       Peropteryx.macrotis 
                        3                       129 
     Pattonomys.carrikeri   Peropteryx.pallidoptera 
                        3                        11 
        Oxymycterus.hiska        Peromyscus.aztecus 
                        3                         6 
    Peropteryx.trinitatis         Peromyscus.beatae 
                        2                         6 
         Philander.deltae     Peromyscus.spicilegus 
                        3                         1 
      Phylloderma.stenops         Peromyscus.furvus 
                      119                         2 
        Oxymycterus.rufus     Phaenomys.ferrugineus 
                       13                         1 
      Peropteryx.kappleri       Peromyscus.eremicus 
                      122                         1 
        Peromyscus.boylii     Phyllomys.blainvillii 
                        1                         8 
       Peromyscus.levipes       Philander.mondolfii 
                        3                        15 
       Philander.frenatus Phyllomys.mantiqueirensis 
                       15                         3 
   Phyllostomus.elongatus       Oxymycterus.nasutus 
                      112                         9 
     Peromyscus.hylocetes      Phyllonycteris.poeyi 
                        2                         1 
      Oxymycterus.roberti   Oxymycterus.dasytrichus 
                       18                         6 
    Oxymycterus.angularis          Phyllomys.medius 
                        8                        10 
      Peromyscus.megalops       Pattonomys.occasius 
                        2                         5 
    Phyllostomus.hastatus           Phyllomys.kerri 
                      126                         1 
     Oxymycterus.quaestor         Phyllops.falcatus 
                       15                         1 
    Peropteryx.leucoptera         Phyllomys.thomasi 
                       59                         1 
   Peromyscus.yucatanicus    Ozotoceros.bezoarticus 
                        1                        41 
        Peromyscus.gratus       Philander.andersoni 
                        3                        25 
      Oxymycterus.delator      Philander.mcilhennyi 
                        4                        11 
   Peromyscus.maniculatus    Peromyscus.winkelmanni 
                        3                         1 
      Peromyscus.gymnotis        Perognathus.flavus 
                        2                         3 
   Oxymycterus.paramensis  Peromyscus.guatemalensis 
                       10                         2 
         Oxymycterus.inca      Perognathus.merriami 
                       13                         1 
    Phyllostomus.discolor     Peromyscus.difficilis 
                      126                         3 
     Peromyscus.melanotis         Phyllomys.lamarum 
                        3                         9 
   Peromyscus.ochraventer   Pattonomys.semivillosus 
                        1                         3 
          Phyllomys.lundi       Pattonomys.flavidus 
                        2                         2 
      Peromyscus.stirtoni         Phyllomys.pattoni 
                        2                         7 
        Phyllomys.sulinus    Peromyscus.melanophrys 
                       11                         4 
     Peromyscus.perfulvus      Oxymycterus.caparoae 
                        2                         4

We can work on that by removing species that occur in less than four sites.

# Remove all columns that have less than 4 presences
to.remove <- colnames(DataSpecies)[colSums(DataSpecies) <= 4]
`%ni%` <- Negate(`%in%`)
DataSpecies <- subset(DataSpecies, select = names(DataSpecies) %ni% to.remove)

# Replace '_' per '.'
names(DataSpecies) <- gsub(x = names(DataSpecies), pattern = "\\.", replacement = ".")

write.table(DataSpecies, "data/matrices/NT_PAM_5.txt")

Species distribution modelling

Initialising and formatting direct input data

First, input data needs to be rearranged for usage in biomod2 using BIOMOD_FormatingData(). Its most important arguments are: * resp.var contains species data (for a single species) in binary format (ones for presences, zeros for true absences and NA for indeterminated) that will be used to build the species distribution models. It may be a vector or a spatial points object. * expl.var contains a matrix, data.frame, SpatialPointsDataFrame or RasterStack containing your explanatory variables. * resp.xy two columns matrix containing the X and Y coordinates of resp.var (only consider if resp.var is a vector) * resp.name contains the response variable name (species).

# Recall your variables again: 

## Load prepared species data
myResp <-  read.table("data/matrices/NT_PAM_5.txt", header=TRUE)

## Load environmental variables
myExpl <-  read.table("data/matrices/NT_EnvVar_5.txt", header=TRUE)
myExpl <- DataSpecies

# Define the species of interest
myRespName <- names(DataSpecies)[1]

# Load coordinates
myRespCoord <- read.table("data/matrices/NT_coords_5.txt", header=TRUE)

myBiomodData <- BIOMOD_FormatingData(resp.var = as.numeric(DataSpecies[,myRespName]),
                                     expl.var = myExpl,
                                     resp.xy = myRespCoord,
                                     resp.name = myRespName,
                                     #PA.nb.rep = 2,
                                     #PA.nb.absences = 200,
                                     #PA.strategy = 'random',
                                     na.rm = TRUE)

-=-=-=-=-=-=-=-=-=-=-= Panthera.onca Data Formating -=-=-=-=-=-=-=-=-=-=-=

> No pseudo absences selection !
      ! No data has been set aside for modeling evaluation
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=

Now, you may check if your data is correctly formatted by printing and ploting the myBiomodData object.

myBiomodData

-=-=-=-=-=-=-=-=-=-=-=-=-= 'BIOMOD.formated.data' -=-=-=-=-=-=-=-=-=-=-=-=-=

sp.name =  Panthera.onca

     50 presences,  13 true absences and  0 undifined points in dataset


     2 explanatory variables

     bio_1            bio_2       
 Min.   : 54.06   Min.   : 80.49  
 1st Qu.:182.15   1st Qu.: 99.89  
 Median :236.43   Median :117.75  
 Mean   :211.69   Mean   :115.79  
 3rd Qu.:256.98   3rd Qu.:128.26  
 Max.   :269.00   Max.   :162.52  

-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
plot(myBiomodData)

Building models

Defining algorithm parameters using default options

We are ready for running our set of models on our species. This step is the core of the modeling procedure.

First, we define the of different options for each modeling technique using the function BIOMOD_ModelingOptions(). If we do not change its parameters, it will take the default values (run Print_Default_ModelingOptions() to see them) defined within biomod2. For the sake of simplicity, we will keep all options default.

myBiomodOption <- BIOMOD_ModelingOptions()

# The created object is then given to BIOMOD_Modeling in the next step.

Computing models

Now, we proceed to running the set of models on our species. We may choose between 12 difeerent algorithms (‘GLM’, ‘GBM’, ‘GAM’, ‘CTA’, ‘ANN’, ‘SRE’, ‘FDA’, ‘MARS’, ‘RF’, ‘MAXENT’).

As we do not have evaluation data, we will make 3-fold cross-validation (number controlled by NbRunEval argument) of our models by randomly splitting our data set into 2 subsets DataSplit.

Take a look below:

myBiomodModelOut <- BIOMOD_Modeling(
    # BIOMOD_FormatingData()-class object
    data = myBiomodData,
    
    # The set of models to be calibrated on the data
    models = c('GBM', 'GLM', 'GAM', 'MAXENT.Tsuruoka', 'RF'), 
    
    # BIOMOD.models.options object 
    models.options = myBiomodOption, 
    
    # Number of evaluation runs
    NbRunEval=3, 
    
    # % of data used to calibrate the models, the remaining part will be used for testing
    DataSplit=70,
    
    # If this argument is kept to NULL, each observation (presence or absence) has the same weight (independent of the number of presences and absences).
    Prevalence=0.5, 
    
    # Number of permutations to estimate variable importance
    VarImport=5,
    
    # Types of evaluations methods to be used. The complete list is within ?BIOMOD_Modeling()
    models.eval.meth = c('TSS','ROC'),
    
    # Keep all results and outputs on hard drive or not. Keep it always TRUE
    SaveObj = TRUE,
    
    # If true, all model predictions will be scaled with a binomial GLM
    rescal.all.models = FALSE,
    
    # If true, models calibrated and evaluated with the whole dataset are done.
    # Building models with all information available may be usefull in some particular 
    # cases (i.e. rare species with few presences points).
    do.full.models = FALSE,
    
    # character, the ID (=name) of modeling procedure. A random number by default
    modeling.id = paste(myRespName,"CurrentClim",sep="")) # We call it 'CurrentClim' because the bioclimatic variables we are working with are from the present climate.


Loading required library...

Checking Models arguments...

Creating suitable Workdir...

    > Automatic weights creation to rise a 0.5 prevalence


-=-=-=-=-=-=-=-=-=-=-= Panthera.onca Modeling Summary -=-=-=-=-=-=-=-=-=-=-=

 38  environmental variables ( Panthera.onca Pecari.tajacu Phyllostomus.latifolius Philander.opossum Phyllomys.nigrispinus Peromyscus.mexicanus Phyllomys.dasythrix Oxymycterus.amazonicus Oxymycterus.hispidus Peropteryx.macrotis Peropteryx.pallidoptera Peromyscus.aztecus Peromyscus.beatae Phylloderma.stenops Oxymycterus.rufus Peropteryx.kappleri Phyllomys.blainvillii Philander.mondolfii Philander.frenatus Phyllostomus.elongatus Oxymycterus.nasutus Oxymycterus.roberti Oxymycterus.dasytrichus Oxymycterus.angularis Phyllomys.medius Pattonomys.occasius Phyllostomus.hastatus Oxymycterus.quaestor Peropteryx.leucoptera Ozotoceros.bezoarticus Philander.andersoni Philander.mcilhennyi Oxymycterus.paramensis Oxymycterus.inca Phyllostomus.discolor Phyllomys.lamarum Phyllomys.pattoni Phyllomys.sulinus )
Number of evaluation repetitions : 3
Models selected : GBM GLM GAM MAXENT.Tsuruoka RF 

Total number of model runs : 15 

-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=


-=-=-=- Run :  Panthera.onca_AllData 


-=-=-=--=-=-=- Panthera.onca_AllData_RUN1 

Model=Generalised Boosting Regression 
     2500 maximum different trees and  3  Fold Cross-Validation
*** inherits(g.pred,'try-error')
   ! Note :  Panthera.onca_AllData_RUN1_GBM failed!

Model=GLM ( quadratic with no interaction )
    Stepwise procedure using AIC criteria
    selected formula : Panthera.onca ~ Philander.opossum + Oxymycterus.paramensis + 
    Oxymycterus.amazonicus + Phyllostomus.latifolius + Pecari.tajacu + 
    Oxymycterus.rufus + Phyllomys.nigrispinus + Philander.mondolfii + 
    Oxymycterus.dasytrichus + Phyllomys.lamarum + Oxymycterus.angularis + 
    Oxymycterus.hispidus + Philander.frenatus + Phyllomys.dasythrix
<environment: 0x000000002341cae0>

    Evaluating Model stuff...
    Evaluating Predictor Contributions... 

Model=GAM
     GAM_mgcv algorithm chosen
    Automatic formula generation...
    > GAM (mgcv) modelling...
*** inherits(g.pred,'try-error')
   ! Note :  Panthera.onca_AllData_RUN1_GAM failed!

Model evaluation

Extracting evaluation scores

We are able now to capture our model evaluations using the get_evaluations function. In this tutorial, we have focused on two evaluations methods: Receiving Operator Curves and True Skill Statistic.

# get all models evaluation
myBiomodModelEval <- get_evaluations(myBiomodModelOut)  #contains model evaluation scores

# print the dimnames of this object
dimnames(myBiomodModelEval)
[[1]]
[1] "TSS" "ROC"

[[2]]
[1] "Testing.data" "Cutoff"       "Sensitivity"  "Specificity" 

[[3]]
[1] "GBM"             "GLM"             "MAXENT.Tsuruoka" "RF"             

[[4]]
[1] "RUN1" "RUN2" "RUN3"

[[5]]
Panthera.onca_AllData 
            "AllData" 
hist(myBiomodModelEval)

write.table(myBiomodModelEval, file = "data/results/EvalAll_Model_Eval")

Plotting evaluation scores

We can also plot evaluation scores for sensitivity and specifity for all models.

# print the Sensitivity of all models --> TSS and ROC scores for SENSITIVITY
mySensitivity_All_Models <- myBiomodModelEval[, "Sensitivity", , , ]
mySensitivity_All_Models
, , RUN1

    GBM GLM MAXENT.Tsuruoka RF
TSS  80  40              90 70
ROC  80  40              90 70

, , RUN2

    GBM GLM MAXENT.Tsuruoka RF
TSS  80  50              60 70
ROC  80  50              60 70

, , RUN3

    GBM GLM MAXENT.Tsuruoka  RF
TSS 100 100              90 100
ROC 100 100              90 100
mySensAll <- as.data.frame(t(as.data.frame(mySensitivity_All_Models)))

MySensHist.TSS <- ggplot(mySensAll, aes(x = TSS)) + geom_histogram(aes(y = ..density..), 
    binwidth = density(mySensAll$TSS)$bw) + geom_density(fill = "red", alpha = 0.2)

MySensHist.ROC <- ggplot(mySensAll, aes(x = ROC)) + geom_histogram(aes(y = ..density..), 
    binwidth = density(mySensAll$ROC)$bw) + geom_density(fill = "red", alpha = 0.2)

MySensHist.TSS

MySensHist.ROC

# save results
write.table(mySensitivity_All_Models, file = "data/results/Sensitivity_All_Models")
, , RUN1

    GBM GLM MAXENT.Tsuruoka  RF
TSS 100 100             100 100
ROC 100 100             100 100

, , RUN2

       GBM GLM MAXENT.Tsuruoka     RF
TSS 66.667 100             100 66.667
ROC 66.667 100             100 66.667

, , RUN3

       GBM    GLM MAXENT.Tsuruoka     RF
TSS 66.667 66.667          66.667 66.667
ROC 66.667 66.667          66.667 66.667

# Proportion of PRESENCES correctly predicted (true positive rate)
sensitivity <- read.table("data/results/Sensitivity_All_Models")

summary(sensitivity)
    GBM.RUN1     GLM.RUN1  MAXENT.Tsuruoka.RUN1    RF.RUN1      GBM.RUN2 
 Min.   :80   Min.   :40   Min.   :90           Min.   :70   Min.   :80  
 1st Qu.:80   1st Qu.:40   1st Qu.:90           1st Qu.:70   1st Qu.:80  
 Median :80   Median :40   Median :90           Median :70   Median :80  
 Mean   :80   Mean   :40   Mean   :90           Mean   :70   Mean   :80  
 3rd Qu.:80   3rd Qu.:40   3rd Qu.:90           3rd Qu.:70   3rd Qu.:80  
 Max.   :80   Max.   :40   Max.   :90           Max.   :70   Max.   :80  
    GLM.RUN2  MAXENT.Tsuruoka.RUN2    RF.RUN2      GBM.RUN3  
 Min.   :50   Min.   :60           Min.   :70   Min.   :100  
 1st Qu.:50   1st Qu.:60           1st Qu.:70   1st Qu.:100  
 Median :50   Median :60           Median :70   Median :100  
 Mean   :50   Mean   :60           Mean   :70   Mean   :100  
 3rd Qu.:50   3rd Qu.:60           3rd Qu.:70   3rd Qu.:100  
 Max.   :50   Max.   :60           Max.   :70   Max.   :100  
    GLM.RUN3   MAXENT.Tsuruoka.RUN3    RF.RUN3   
 Min.   :100   Min.   :90           Min.   :100  
 1st Qu.:100   1st Qu.:90           1st Qu.:100  
 Median :100   Median :90           Median :100  
 Mean   :100   Mean   :90           Mean   :100  
 3rd Qu.:100   3rd Qu.:90           3rd Qu.:100  
 Max.   :100   Max.   :90           Max.   :100  
str(sensitivity)
'data.frame':   2 obs. of  12 variables:
 $ GBM.RUN1            : int  80 80
 $ GLM.RUN1            : int  40 40
 $ MAXENT.Tsuruoka.RUN1: int  90 90
 $ RF.RUN1             : int  70 70
 $ GBM.RUN2            : int  80 80
 $ GLM.RUN2            : int  50 50
 $ MAXENT.Tsuruoka.RUN2: int  60 60
 $ RF.RUN2             : int  70 70
 $ GBM.RUN3            : int  100 100
 $ GLM.RUN3            : int  100 100
 $ MAXENT.Tsuruoka.RUN3: int  90 90
 $ RF.RUN3             : int  100 100
sensitivity  #table of sensitivity by TSS or ROC per run per model
    GBM.RUN1 GLM.RUN1 MAXENT.Tsuruoka.RUN1 RF.RUN1 GBM.RUN2 GLM.RUN2
TSS       80       40                   90      70       80       50
ROC       80       40                   90      70       80       50
    MAXENT.Tsuruoka.RUN2 RF.RUN2 GBM.RUN3 GLM.RUN3 MAXENT.Tsuruoka.RUN3
TSS                   60      70      100      100                   90
ROC                   60      70      100      100                   90
    RF.RUN3
TSS     100
ROC     100
sensTSS.plot <- ggplot(sensData, aes(x = model1, y = TSS, fill = model1)) + 
    geom_boxplot() + guides(fill = FALSE) + ggtitle("Sensitivity using TSS scores") + 
    theme(plot.subtitle = element_text(vjust = 1), plot.caption = element_text(vjust = 1), 
        panel.background = element_rect(fill = NA)) + labs(x = "Model Type", 
    y = "TSS (%)")

sensTSS.plot <- ggplotly(sensTSS.plot)

sensROC.plot <- ggplot(sensData, aes(x = model2, y = ROC, fill = model1)) + 
    geom_boxplot() + guides(fill = FALSE) + ggtitle("Sensitivity using ROC scores") + 
    theme(plot.subtitle = element_text(vjust = 1), plot.caption = element_text(vjust = 1), 
        panel.background = element_rect(fill = NA)) + labs(x = "Model Type", 
    y = "ROC (%)")

sensROC.plot <- ggplotly(sensROC.plot)

sensTSS.plot
sensROC.plot

Discussion: How do the models compare by their sensitivity scores?

##### plot SPECIFICITY #####

# Proportion of ABSENCES correctly predicted (true negative rate)

specificity <- read.table("data/results/Specificity_All_Models")
summary(specificity)
    GBM.RUN1      GLM.RUN1   MAXENT.Tsuruoka.RUN1    RF.RUN1   
 Min.   :100   Min.   :100   Min.   :100          Min.   :100  
 1st Qu.:100   1st Qu.:100   1st Qu.:100          1st Qu.:100  
 Median :100   Median :100   Median :100          Median :100  
 Mean   :100   Mean   :100   Mean   :100          Mean   :100  
 3rd Qu.:100   3rd Qu.:100   3rd Qu.:100          3rd Qu.:100  
 Max.   :100   Max.   :100   Max.   :100          Max.   :100  
    GBM.RUN2        GLM.RUN2   MAXENT.Tsuruoka.RUN2    RF.RUN2     
 Min.   :66.67   Min.   :100   Min.   :100          Min.   :66.67  
 1st Qu.:66.67   1st Qu.:100   1st Qu.:100          1st Qu.:66.67  
 Median :66.67   Median :100   Median :100          Median :66.67  
 Mean   :66.67   Mean   :100   Mean   :100          Mean   :66.67  
 3rd Qu.:66.67   3rd Qu.:100   3rd Qu.:100          3rd Qu.:66.67  
 Max.   :66.67   Max.   :100   Max.   :100          Max.   :66.67  
    GBM.RUN3        GLM.RUN3     MAXENT.Tsuruoka.RUN3    RF.RUN3     
 Min.   :66.67   Min.   :66.67   Min.   :66.67        Min.   :66.67  
 1st Qu.:66.67   1st Qu.:66.67   1st Qu.:66.67        1st Qu.:66.67  
 Median :66.67   Median :66.67   Median :66.67        Median :66.67  
 Mean   :66.67   Mean   :66.67   Mean   :66.67        Mean   :66.67  
 3rd Qu.:66.67   3rd Qu.:66.67   3rd Qu.:66.67        3rd Qu.:66.67  
 Max.   :66.67   Max.   :66.67   Max.   :66.67        Max.   :66.67  
str(specificity)
'data.frame':   2 obs. of  12 variables:
 $ GBM.RUN1            : int  100 100
 $ GLM.RUN1            : int  100 100
 $ MAXENT.Tsuruoka.RUN1: int  100 100
 $ RF.RUN1             : int  100 100
 $ GBM.RUN2            : num  66.7 66.7
 $ GLM.RUN2            : int  100 100
 $ MAXENT.Tsuruoka.RUN2: int  100 100
 $ RF.RUN2             : num  66.7 66.7
 $ GBM.RUN3            : num  66.7 66.7
 $ GLM.RUN3            : num  66.7 66.7
 $ MAXENT.Tsuruoka.RUN3: num  66.7 66.7
 $ RF.RUN3             : num  66.7 66.7
specificity  #table of specificity by TSS or ROC per run per model
    GBM.RUN1 GLM.RUN1 MAXENT.Tsuruoka.RUN1 RF.RUN1 GBM.RUN2 GLM.RUN2
TSS      100      100                  100     100   66.667      100
ROC      100      100                  100     100   66.667      100
    MAXENT.Tsuruoka.RUN2 RF.RUN2 GBM.RUN3 GLM.RUN3 MAXENT.Tsuruoka.RUN3
TSS                  100  66.667   66.667   66.667               66.667
ROC                  100  66.667   66.667   66.667               66.667
    RF.RUN3
TSS  66.667
ROC  66.667
specTSS <- specificity[1, ]
specTran <- t(specTSS)
modelNames <- rownames(specTran)
model1 <- substr(modelNames, 1, 3)
specPlot1 <- data.frame(specTran, model1)

specROC <- specificity[2, ]
specTran <- t(specROC)
modelNames <- rownames(specTran)
model2 <- substr(modelNames, 1, 3)
specPlot2 <- data.frame(specTran, model2)

specData <- cbind(specPlot1, specPlot2)

Let’s take a look at our plots for specificity:

specTSS.plot <- ggplot(specData, aes(x = model1, y = TSS, fill = model1)) + 
    geom_boxplot() + guides(fill = FALSE) + ggtitle("Specificity using TSS scores") + 
    theme(plot.subtitle = element_text(vjust = 1), plot.caption = element_text(vjust = 1), 
        panel.background = element_rect(fill = NA)) + labs(x = "Model Type", 
    y = "TSS (%)")

specROC.plot <- ggplot(specData, aes(x = model2, y = ROC, fill = model1)) + 
    geom_boxplot() + guides(fill = FALSE) + ggtitle("Specificity using ROC scores") + 
    theme(plot.subtitle = element_text(vjust = 1), plot.caption = element_text(vjust = 1), 
        panel.background = element_rect(fill = NA)) + labs(x = "Model Type", 
    y = "ROC (%)")

# Make them interactive!
specTSS.plot <- ggplotly(specTSS.plot)
specROC.plot <- ggplotly(specROC.plot)

# Plot them
specTSS.plot
specROC.plot

We can also print the variable importance for all models

# print variable importances
get_variables_importance(myBiomodModelOut)
, , RUN1, AllData

      GBM   GLM GAM MAXENT.Tsuruoka    RF
 [1,]  NA 0.000  NA           0.986 0.000
 [2,]  NA 0.082  NA           0.069 0.031
 [3,]  NA 0.056  NA           0.005 0.000
 [4,]  NA 0.600  NA           0.068 0.044
 [5,]  NA 0.047  NA           0.000 0.000
 [6,]  NA 0.000  NA           0.000 0.000
 [7,]  NA 0.007  NA           0.000 0.000
 [8,]  NA 0.063  NA           0.010 0.000
 [9,]  NA 0.127  NA           0.000 0.000
[10,]  NA 0.000  NA           0.015 0.030
[11,]  NA 0.000  NA           0.000 0.000
[12,]  NA 0.000  NA           0.000 0.000
[13,]  NA 0.000  NA           0.000 0.000
[14,]  NA 0.000  NA           0.011 0.006
[15,]  NA 0.036  NA           0.018 0.005

 [ reached getOption("max.print") -- omitted 23 row(s) and 2 matrix slice(s) ]
### save models evaluation scores and variables importance on hard drive
capture.output(get_evaluations(myBiomodModelOut), file = file.path(myRespName, 
    paste(myRespName, "_formal_models_eval.txt", sep = "")))

capture.output(get_variables_importance(myBiomodModelOut), file = file.path(myRespName, 
    paste(myRespName, "_formal_models_var_imp.txt", sep = "")))

Ensemble of forecasting

One powerful feature from biomod2comes with BIOMOD_EnsembleModeling, which combines individual models to build an ensemble of forecast. In the following example, we exclude all models having a TSS score lower than 0.5.

  ### Building ensemble-models
  myBiomodEM <- BIOMOD_EnsembleModeling(
    # a "BIOMOD.models.out" returned by BIOMOD_Modeling
    modeling.output = myBiomodModelOut,
    
    # a character vector (either 'all' or a sub-selection of model names) that defines the models kept for building the ensemble model
    chosen.models = 'all',
    
    # The value chosen for this parameter will control the number of ensemble models built.
    # 'all' means a total consensus model will be built
    em.by='all',
    
    # Evaluation metric used to build ensemble models
    eval.metric = 'all',
    
    # If not NULL, the minimum scores below which models will be excluded of the ensemble-models building
    eval.metric.quality.threshold = c(0.7, 0.5),
    
    # Estimates the ... across predictions
    prob.mean = T,
    prob.cv = T,
    prob.ci = T,
    prob.ci.alpha = 0.05,
    prob.median = T,
    committee.averaging = T,
    prob.mean.weight = T,
    prob.mean.weight.decay = 'proportional')

-=-=-=-=-=-=-=-=-=-=-=-=-= Build Ensemble Models -=-=-=-=-=-=-=-=-=-=-=-=-=

   ! all models available will be included in ensemble.modeling
   > Evaluation & Weighting methods summary :
      TSS over 0.7
      ROC over 0.5


  > mergedAlgo_mergedRun_mergedData ensemble modeling
   ! Models projections for whole zonation required...
    > Projecting Panthera.onca_AllData_RUN1_GLM ...

You can easily access to the data and outputs of myBiomodEM using some specific functions to make your life easier.

Projecting your outputs

### Make projections on current variable
myBiomodProj <- BIOMOD_Projection(modeling.output = myBiomodModelOut, new.env = myExpl, 
    xy.new.env = myRespCoord, proj.name = "current", selected.models = "all", 
    binary.meth = "TSS", compress = TRUE, clamping.mask = F, output.format = ".RData")

-=-=-=-=-=-=-=-=-=-=-=-=-= Do Models Projections -=-=-=-=-=-=-=-=-=-=-=-=-=

    > Building clamping mask

*** in setMethod('BinaryTransformation', signature(data='numeric')
*** in setMethod('BinaryTransformation', signature(data='data.frame')
*** in setMethod('BinaryTransformation', signature(data='numeric')
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*** in setMethod('BinaryTransformation', signature(data='data.frame')
    > Projecting Panthera.onca_AllData_RUN1_GLM ...
myCurrentProj <- getProjection(myBiomodProj)

!! deprecated function that will be remove in next package update
 please prefere to use get_predictions(obj)
myCurrentProj
, , RUN1, AllData

        GLM MAXENT.Tsuruoka   RF
  [1,]  400            1000  390
  [2,]  400            1000  286
  [3,] 1000            1000  864
  [4,] 1000            1000  918
  [5,]    0             500    0
  [6,] 1000            1000  940
  [7,] 1000            1000 1000
  [8,] 1000            1000 1000
  [9,] 1000            1000 1000
 [10,] 1000            1000  998
 [11,] 1000            1000 1000
 [12,] 1000            1000 1000
 [13,]  400            1000  920
 [14,] 1000            1000 1000
 [15,] 1000            1000  998
 [16,] 1000            1000 1000
 [17,] 1000            1000 1000
 [18,] 1000            1000 1000
 [19,] 1000            1000  996
 [20,] 1000            1000  994
 [21,] 1000            1000  990
 [22,] 1000            1000 1000
 [23,] 1000            1000  996
 [24,] 1000            1000  996
 [25,] 1000            1000 1000

 [ reached getOption("max.print") -- omitted 158 row(s) and 2 matrix slice(s) ]
### Make ensemble-models projections on current variable
myBiomodEF <- BIOMOD_EnsembleForecasting(projection.output = myBiomodProj, binary.meth = "TSS", 
    total.consensus = TRUE, EM.output = myBiomodEM)

-=-=-=-=-=-=-=-=-=-=-= Do Ensemble Models Projections -=-=-=-=-=-=-=-=-=-=-=


    > Projecting Panthera.onca_EMmeanByTSS_mergedAlgo_mergedRun_mergedData ...
*** here!!
'data.frame':   183 obs. of  9 variables:
 $ Panthera.onca_AllData_RUN1_GLM            : num  400 400 1000 1000 0 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_RF             : num  390 286 864 918 0 940 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_RF             : num  308 202 922 974 2 992 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN3_GLM            : num  1000 25 1000 1000 25 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_RF             : num  678 312 962 970 6 986 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_GLM            : num  366 366 1000 1000 0 1000 1000 1000 1000 1000 ...
NULL

***
Panthera.onca_AllData_RUN1_GLM Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka Panthera.onca_AllData_RUN1_RF Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka Panthera.onca_AllData_RUN2_RF Panthera.onca_AllData_RUN3_GLM Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka Panthera.onca_AllData_RUN3_RF
*** here!!


    > Projecting Panthera.onca_EMcvByTSS_mergedAlgo_mergedRun_mergedData ...
*** here!!
'data.frame':   183 obs. of  9 variables:
 $ Panthera.onca_AllData_RUN1_GLM            : num  400 400 1000 1000 0 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_RF             : num  390 286 864 918 0 940 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_RF             : num  308 202 922 974 2 992 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN3_GLM            : num  1000 25 1000 1000 25 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_RF             : num  678 312 962 970 6 986 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_GLM            : num  366 366 1000 1000 0 1000 1000 1000 1000 1000 ...
NULL

***
Panthera.onca_AllData_RUN1_GLM Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka Panthera.onca_AllData_RUN1_RF Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka Panthera.onca_AllData_RUN2_RF Panthera.onca_AllData_RUN3_GLM Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka Panthera.onca_AllData_RUN3_RF
*** here!!


    > Projecting Panthera.onca_EMciInfByTSS_mergedAlgo_mergedRun_mergedData ...
*** here!!
'data.frame':   183 obs. of  9 variables:
 $ Panthera.onca_AllData_RUN1_GLM            : num  400 400 1000 1000 0 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_RF             : num  390 286 864 918 0 940 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_RF             : num  308 202 922 974 2 992 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN3_GLM            : num  1000 25 1000 1000 25 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_RF             : num  678 312 962 970 6 986 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_GLM            : num  366 366 1000 1000 0 1000 1000 1000 1000 1000 ...
NULL

***
Panthera.onca_AllData_RUN1_GLM Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka Panthera.onca_AllData_RUN1_RF Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka Panthera.onca_AllData_RUN2_RF Panthera.onca_AllData_RUN3_GLM Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka Panthera.onca_AllData_RUN3_RF
*** here!!


    > Projecting Panthera.onca_EMciSupByTSS_mergedAlgo_mergedRun_mergedData ...
*** here!!
'data.frame':   183 obs. of  9 variables:
 $ Panthera.onca_AllData_RUN1_GLM            : num  400 400 1000 1000 0 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_RF             : num  390 286 864 918 0 940 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_RF             : num  308 202 922 974 2 992 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN3_GLM            : num  1000 25 1000 1000 25 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_RF             : num  678 312 962 970 6 986 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_GLM            : num  366 366 1000 1000 0 1000 1000 1000 1000 1000 ...
NULL

***
Panthera.onca_AllData_RUN1_GLM Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka Panthera.onca_AllData_RUN1_RF Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka Panthera.onca_AllData_RUN2_RF Panthera.onca_AllData_RUN3_GLM Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka Panthera.onca_AllData_RUN3_RF
*** here!!


    > Projecting Panthera.onca_EMmedianByTSS_mergedAlgo_mergedRun_mergedData ...
*** here!!
'data.frame':   183 obs. of  9 variables:
 $ Panthera.onca_AllData_RUN1_GLM            : num  400 400 1000 1000 0 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_RF             : num  390 286 864 918 0 940 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_RF             : num  308 202 922 974 2 992 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN3_GLM            : num  1000 25 1000 1000 25 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_RF             : num  678 312 962 970 6 986 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_GLM            : num  366 366 1000 1000 0 1000 1000 1000 1000 1000 ...
NULL

***
Panthera.onca_AllData_RUN1_GLM Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka Panthera.onca_AllData_RUN1_RF Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka Panthera.onca_AllData_RUN2_RF Panthera.onca_AllData_RUN3_GLM Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka Panthera.onca_AllData_RUN3_RF
*** here!!


    > Projecting Panthera.onca_EMcaByTSS_mergedAlgo_mergedRun_mergedData ...
*** here!!
'data.frame':   183 obs. of  9 variables:
 $ Panthera.onca_AllData_RUN1_GLM            : num  400 400 1000 1000 0 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_RF             : num  390 286 864 918 0 940 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_RF             : num  308 202 922 974 2 992 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN3_GLM            : num  1000 25 1000 1000 25 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_RF             : num  678 312 962 970 6 986 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_GLM            : num  366 366 1000 1000 0 1000 1000 1000 1000 1000 ...
NULL

***
Panthera.onca_AllData_RUN1_GLM Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka Panthera.onca_AllData_RUN1_RF Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka Panthera.onca_AllData_RUN2_RF Panthera.onca_AllData_RUN3_GLM Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka Panthera.onca_AllData_RUN3_RF
*** here!!

*** in setMethod('BinaryTransformation', signature(data='data.frame')

    > Projecting Panthera.onca_EMwmeanByTSS_mergedAlgo_mergedRun_mergedData ...
*** here!!
'data.frame':   183 obs. of  9 variables:
 $ Panthera.onca_AllData_RUN1_GLM            : num  400 400 1000 1000 0 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_RF             : num  390 286 864 918 0 940 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_RF             : num  308 202 922 974 2 992 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN3_GLM            : num  1000 25 1000 1000 25 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_RF             : num  678 312 962 970 6 986 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_GLM            : num  366 366 1000 1000 0 1000 1000 1000 1000 1000 ...
NULL

***
Panthera.onca_AllData_RUN1_GLM Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka Panthera.onca_AllData_RUN1_RF Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka Panthera.onca_AllData_RUN2_RF Panthera.onca_AllData_RUN3_GLM Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka Panthera.onca_AllData_RUN3_RF
*** here!!


    > Projecting Panthera.onca_EMmeanByROC_mergedAlgo_mergedRun_mergedData ...
*** here!!
'data.frame':   183 obs. of  9 variables:
 $ Panthera.onca_AllData_RUN1_GLM            : num  400 400 1000 1000 0 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_RF             : num  390 286 864 918 0 940 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_RF             : num  308 202 922 974 2 992 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN3_GLM            : num  1000 25 1000 1000 25 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_RF             : num  678 312 962 970 6 986 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_GLM            : num  366 366 1000 1000 0 1000 1000 1000 1000 1000 ...
NULL

***
Panthera.onca_AllData_RUN1_GLM Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka Panthera.onca_AllData_RUN1_RF Panthera.onca_AllData_RUN2_GLM Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka Panthera.onca_AllData_RUN2_RF Panthera.onca_AllData_RUN3_GLM Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka Panthera.onca_AllData_RUN3_RF
*** here!!


    > Projecting Panthera.onca_EMcvByROC_mergedAlgo_mergedRun_mergedData ...
*** here!!
'data.frame':   183 obs. of  9 variables:
 $ Panthera.onca_AllData_RUN1_GLM            : num  400 400 1000 1000 0 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_RF             : num  390 286 864 918 0 940 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_RF             : num  308 202 922 974 2 992 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN3_GLM            : num  1000 25 1000 1000 25 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_RF             : num  678 312 962 970 6 986 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_GLM            : num  366 366 1000 1000 0 1000 1000 1000 1000 1000 ...
NULL

***
Panthera.onca_AllData_RUN1_GLM Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka Panthera.onca_AllData_RUN1_RF Panthera.onca_AllData_RUN2_GLM Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka Panthera.onca_AllData_RUN2_RF Panthera.onca_AllData_RUN3_GLM Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka Panthera.onca_AllData_RUN3_RF
*** here!!


    > Projecting Panthera.onca_EMciInfByROC_mergedAlgo_mergedRun_mergedData ...
*** here!!
'data.frame':   183 obs. of  9 variables:
 $ Panthera.onca_AllData_RUN1_GLM            : num  400 400 1000 1000 0 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_RF             : num  390 286 864 918 0 940 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_RF             : num  308 202 922 974 2 992 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN3_GLM            : num  1000 25 1000 1000 25 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_RF             : num  678 312 962 970 6 986 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_GLM            : num  366 366 1000 1000 0 1000 1000 1000 1000 1000 ...
NULL

***
Panthera.onca_AllData_RUN1_GLM Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka Panthera.onca_AllData_RUN1_RF Panthera.onca_AllData_RUN2_GLM Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka Panthera.onca_AllData_RUN2_RF Panthera.onca_AllData_RUN3_GLM Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka Panthera.onca_AllData_RUN3_RF
*** here!!


    > Projecting Panthera.onca_EMciSupByROC_mergedAlgo_mergedRun_mergedData ...
*** here!!
'data.frame':   183 obs. of  9 variables:
 $ Panthera.onca_AllData_RUN1_GLM            : num  400 400 1000 1000 0 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_RF             : num  390 286 864 918 0 940 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_RF             : num  308 202 922 974 2 992 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN3_GLM            : num  1000 25 1000 1000 25 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_RF             : num  678 312 962 970 6 986 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_GLM            : num  366 366 1000 1000 0 1000 1000 1000 1000 1000 ...
NULL

***
Panthera.onca_AllData_RUN1_GLM Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka Panthera.onca_AllData_RUN1_RF Panthera.onca_AllData_RUN2_GLM Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka Panthera.onca_AllData_RUN2_RF Panthera.onca_AllData_RUN3_GLM Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka Panthera.onca_AllData_RUN3_RF
*** here!!


    > Projecting Panthera.onca_EMmedianByROC_mergedAlgo_mergedRun_mergedData ...
*** here!!
'data.frame':   183 obs. of  9 variables:
 $ Panthera.onca_AllData_RUN1_GLM            : num  400 400 1000 1000 0 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_RF             : num  390 286 864 918 0 940 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_RF             : num  308 202 922 974 2 992 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN3_GLM            : num  1000 25 1000 1000 25 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_RF             : num  678 312 962 970 6 986 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_GLM            : num  366 366 1000 1000 0 1000 1000 1000 1000 1000 ...
NULL

***
Panthera.onca_AllData_RUN1_GLM Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka Panthera.onca_AllData_RUN1_RF Panthera.onca_AllData_RUN2_GLM Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka Panthera.onca_AllData_RUN2_RF Panthera.onca_AllData_RUN3_GLM Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka Panthera.onca_AllData_RUN3_RF
*** here!!


    > Projecting Panthera.onca_EMcaByROC_mergedAlgo_mergedRun_mergedData ...
*** here!!
'data.frame':   183 obs. of  9 variables:
 $ Panthera.onca_AllData_RUN1_GLM            : num  400 400 1000 1000 0 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_RF             : num  390 286 864 918 0 940 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_RF             : num  308 202 922 974 2 992 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN3_GLM            : num  1000 25 1000 1000 25 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_RF             : num  678 312 962 970 6 986 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_GLM            : num  366 366 1000 1000 0 1000 1000 1000 1000 1000 ...
NULL

***
Panthera.onca_AllData_RUN1_GLM Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka Panthera.onca_AllData_RUN1_RF Panthera.onca_AllData_RUN2_GLM Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka Panthera.onca_AllData_RUN2_RF Panthera.onca_AllData_RUN3_GLM Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka Panthera.onca_AllData_RUN3_RF
*** here!!

*** in setMethod('BinaryTransformation', signature(data='data.frame')

    > Projecting Panthera.onca_EMwmeanByROC_mergedAlgo_mergedRun_mergedData ...
*** here!!
'data.frame':   183 obs. of  9 variables:
 $ Panthera.onca_AllData_RUN1_GLM            : num  400 400 1000 1000 0 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN1_RF             : num  390 286 864 918 0 940 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_RF             : num  308 202 922 974 2 992 1000 1000 1000 998 ...
 $ Panthera.onca_AllData_RUN3_GLM            : num  1000 25 1000 1000 25 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka: num  1000 1000 1000 1000 500 1000 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN3_RF             : num  678 312 962 970 6 986 1000 1000 1000 1000 ...
 $ Panthera.onca_AllData_RUN2_GLM            : num  366 366 1000 1000 0 1000 1000 1000 1000 1000 ...
NULL

***
Panthera.onca_AllData_RUN1_GLM Panthera.onca_AllData_RUN1_MAXENT.Tsuruoka Panthera.onca_AllData_RUN1_RF Panthera.onca_AllData_RUN2_GLM Panthera.onca_AllData_RUN2_MAXENT.Tsuruoka Panthera.onca_AllData_RUN2_RF Panthera.onca_AllData_RUN3_GLM Panthera.onca_AllData_RUN3_MAXENT.Tsuruoka Panthera.onca_AllData_RUN3_RF
*** here!!


    > Building TSS binaries
*** in setMethod('BinaryTransformation', signature(data='matrix')
*** in setMethod('BinaryTransformation', signature(data='data.frame')

-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
plot(myBiomodEF)

Multi-species modelling

Now, that we know how to do single species modelling, what changes if we want to model the distribution of n species?

biomod2 has no function that allows multi-species to be modelled. But, we can use the help of other R functions to deal with this! For example, we can create a for loop that will execute the modelling parts for each species within our species data frame. See below, (but do not execute it yet!)

# define the species of interest
sp.names <- names(DataSpecies)[1:5]

for (sp.n in sp.names) {
    
    myRespName = sp.n
    cat("\n", myRespName, "modelling...")
    
    ### definition of data i.e keep only the column of our species
    myResp <- as.numeric(DataSpecies[, myRespName])
    myRespCoord = myRespCoord
    
    ### Initialisation
    myBiomodData <- BIOMOD_FormatingData(resp.var = myResp, expl.var = myExpl, 
        resp.xy = myRespCoord, resp.name = myRespName)
    
    ### Options definition
    myBiomodOption <- BIOMOD_ModelingOptions()
    
    ### Modelling
    myBiomodModelOut <- BIOMOD_Modeling(myBiomodData, models = c("GBM", "GLM", 
        "MAXENT.Tsuruoka", "RF"), models.options = myBiomodOption, NbRunEval = 3, 
        DataSplit = 80, Prevalence = 0.5, VarImport = 3, models.eval.meth = c("TSS", 
            "ROC"), SaveObj = TRUE, rescal.all.models = FALSE, do.full.models = FALSE, 
        modeling.id = paste(myRespName, "CurrentClim", sep = ""))
    
    ### save models evaluation scores and variables importance on hard drive
    capture.output(get_evaluations(myBiomodModelOut), file = file.path(myRespName, 
        paste(myRespName, "_formal_models_eval.txt", sep = "")))
    capture.output(get_variables_importance(myBiomodModelOut), file = file.path(myRespName, 
        paste(myRespName, "_formal_models_var_imp.txt", sep = "")))
    
    ### Building ensemble-models
    myBiomodEM <- BIOMOD_EnsembleModeling(modeling.output = myBiomodModelOut, 
        chosen.models = "all", em.by = "all", eval.metric = "all", eval.metric.quality.threshold = c(0.7), 
        prob.mean = T, prob.cv = T, prob.ci = T, prob.ci.alpha = 0.05, prob.median = T, 
        committee.averaging = T, prob.mean.weight = T, prob.mean.weight.decay = "proportional")
    
    ### Make projections on current variable
    myBiomodProj <- BIOMOD_Projection(modeling.output = myBiomodModelOut, new.env = myExpl, 
        xy.new.env = myRespCoord, proj.name = "current", selected.models = "all", 
        binary.meth = "TSS", compress = TRUE, clamping.mask = F, output.format = ".RData")
    
    ### Make ensemble-models projections on current variable
    myBiomodEF <- BIOMOD_EnsembleForecasting(projection.output = myBiomodProj, 
        binary.meth = "TSS", total.consensus = TRUE, EM.output = myBiomodEM)
}

One other way (faster, and perhaps, more elegant) for doing multi-species SDM using biomod2, is to use a apply family function. Like this one:

DIY: Create your own SDM!

### Make ensemble-models projections on current variable load the first speces
### binary maps which will define the mask

alphaMap <- reclassify(subset(myExpl, 1), c(-Inf, Inf, 0))

alphaMap <- get(load(paste(getwd(), "/", myRespName[1], "/proj_current/", "proj_current_", 
    myRespName[1], "_ensemble_TSSbin.RData", sep = "")))[, 1]

Pedro Henrique Braga and Julia Nordlund

April 23, 2017